code: def generate_dummy_inputs(
      self,
      tokenizer: PreTrainedTokenizer,
      batch_size: int = -1,
      seq_length: int = -1,
      is_pair: bool = False,
      framework: Optional[TensorType] = None,
  ) -> Mapping[str, Any]:
      if self.task in ["default", "seq2seq-lm"]:
          common_inputs = (
              self._generate_dummy_inputs_for_default_and_seq2seq_lm(
                  tokenizer,
                  batch_size=batch_size,
                  seq_length=seq_length,
                  is_pair=is_pair,
                  framework=framework,
              )
          )
  
      elif self.task == "causal-lm":
          common_inputs = self._generate_dummy_inputs_for_causal_lm(
              tokenizer,
              batch_size=batch_size,
              seq_length=seq_length,
              is_pair=is_pair,
              framework=framework,
          )
      else:
          common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              tokenizer,
              batch_size=batch_size,
              seq_length=seq_length,
              is_pair=is_pair,
              framework=framework,
          )
  
      return common_inputs
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/mbart/configuration_mbart.py
imports: from collections import OrderedDict
  from typing import Any, Mapping, Optional
  from ...None import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
label_desc: Use image node_id 6 for calling the MBartOnnxConfig obj's underlying member method code with example usage: obj.generate_dummy_inputs(tokenizer, batch_size, seq_length, is_pair, framework) and returns: common_inputs